ノイズを含む点群データを用いた果樹園の樹木構造解析のためのグラフベース手法
Graph-based methods for analyzing orchard tree structure using noisy point cloud data
手持ち・移動式LiDARの低品質点群から、グラフベース手法で個々の樹木の位置特定・セグメンテーション・葉/構造物分類を行う手法を提案し、既存手法より高精度かつ高速であることを示した。
著者: Fredrik Westling, Dr James Underwood, Dr Mitch Bryson
分類: cs.CV, cs.RO
原文アブストラクト
Digitisation of fruit trees using LiDAR enables analysis which can be used to better growing practices to improve yield. Sophisticated analysis requires geometric and semantic understanding of the data, including the ability to discern individual trees as well as identifying leafy and structural matter. Extraction of this information should be rapid, as should data capture, so that entire orchards can be processed, but existing methods for classification and segmentation rely on high-quality data or additional data sources like cameras. We present a method for analysis of LiDAR data specifically for individual tree location, segmentation and matter classification, which can operate on low-quality data captured by handheld or mobile LiDAR. Our methods for tree location and segmentation improved on existing methods with an F1 score of 0.774 and a v-measure of 0.915 respectively, while trunk matter classification performed poorly in absolute terms with an average F1 score of 0.490 on real data, though consistently outperformed existing methods and displayed a significantly shorter runtime.